Recently, deep learning (DL) [1]} has demonstrated better performance than traditional machine learning models in many artificial intelligence fields, including computer vision [2]} and natural language processing [3]}. With the rapid development of DL techniques, numerous DL-based methods have been proposed for SISR, continuously prompting the State-Of-The-Art (SOTA) forward. Like other image transformation tasks, the SISR task can generally be divided into three steps: feature extraction and representation, non-linear mapping, and image reconstruction [4]}. In traditional numerical models, it is time-consuming and inefficient to design an algorithm satisfying all these processes. On the contrary, DL can transfer the SISR task to an almost end-to-end framework incorporating all these three processes, which can greatly decrease manual and computing expense [5]}. Additionally, given the ill-posed nature of SISR which can lead to unstable and hard convergence on the results, DL can alleviate this issue through efficient network architecture and loss functions design. Moreover, modern GPU enables deeper and more complex DL models to train fast, which show greater representation power than traditional numerical models.
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